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All Journal (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
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Perancangan Ontologi Semantik: Representasi Digital Film Bioskop Indonesia Andien Rachma Fadillah; Komang Kartika Noviyanti; I Putu Agus Arya Wiguna; Cokorda Pramartha
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 4 (2024): JELIKU Volume 12 No 4, May 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2024.v12.i04.p20

Abstract

The rapid growth of the film industry in Indonesia has created complexity in the management and integration of cinema-related data. Despite the increase in film production, audience enthusiasm remains unmet due to inadequate film infrastructure and difficulties in accessing information. Semantic Web technology can be a solution by expanding the current web to provide better-defined meanings to information, enabling collaboration between humans and computers. This research aims to develop an ontology for Indonesian cinema to enhance the representation and interoperability of information. Ontology, as a model of structured knowledge representation using the Web Ontology Language (OWL), allows machines to better understand information. The ontology focuses on crucial aspects such as film titles, genres, film crew information, release years, age ratings, and more. It is expected that this ontology provides a solid foundation for more efficient data search, analysis, and processing applications to support the Indonesian entertainment industry. Initial testing involves user questions, and it is anticipated that this ontology model can systematically present national cinema information.
Klasifikasi Kualitas Air Layak Minum menggunakan Algoritma Random Forest Classifier dan GridsearchCV Gusti Agung Diah Sri Ari Ningsih; Cokorda Pramartha
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 13 No 1 (2024): JELIKU Volume 13 No 1, August 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

Drinkable water is water that is healthy for humans to drink and does not pose significant health risks. To determine whether water has a quality that meets health standards can be determined through the substances or minerals contained in it. Conventional methods require quite a long time to evaluate and classify water quality as suitable for consumption or not. One approach that can be used to overcome this problem is to utilize machine learning. This research uses a random forest to carry out classification. Using random forest by default cannot produce optimal performance because the parameters used are not necessarily the best. Therefore, this research also uses GridsearchCV to find optimal hyperparameter values in the Random Forest Classifier. After hyperparameter tuning, an optimal model was obtained with each parameter n_estimators 100, max_depth 9, max_features 4, and min_samples_split 2. The performance of Random Forest after hyperparameter tuning increased accuracy, which was initially 76% increase to 84%, precision which was initially 76.19% increase to 81.70%, recall which was initially 74.89% increase to 85.53%, and f1-score which was initially 75.53%, increase to 83.57%. Keywords: Classification, Drinking Water Quality, Random Forest, Optimal Hyperparameters, Hyperparameter Tuning, GridSearchCV
Penerapan SVM dengan Seleksi Fitur Mutual Information untuk Memprediksi Sentimen PEMILU 2024 I Gusti Bgs Darmika Putra; Cokorda Pramartha; Anak Agung Istri Ngurah Eka Karyawati; Made Agung Raharja
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 4 (2024): JELIKU Volume 12 No 4, May 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2024.v12.i04.p11

Abstract

A wealth of information on the 2024 Indonesian Election floods Twitter, from campaign schedules to candidate profiles and the latest survey results indicating candidate popularity. This information overload poses challenges in discerning comments' sentiment. Manual classification is feasible but time-consuming. Hence, this study aims to streamline data analysis for the 2024 Election. It employs a dataset of 1000 entries categorized as positive or negative. Support Vector Machine (SVM) with Mutual Information feature selection is utilized for classification. Results reveal that Mutual Information feature selection enhances SVM performance. Without it, SVM achieves 88% accuracy and 87.9% f-measure using the rbf kernel (C=1, ?=2), computed in about 0.07 seconds. With feature selection, SVM's accuracy improves to 90%, and f-measure to 89.9% with 60% features, using rbf kernel (C=10, ?=0.5), reducing computation time to 0.02 seconds, optimizing both performance and efficiency. The website system scored 88.63 in usability, higher than the global average of 68, based on a SUS questionnaire with 10 questions and 20 respondents. This indicates excellent performance and user satisfaction, as evaluated from the web system.
Klasifikasi Serangan Application Layer Denial of Service Menggunakan Support Vector Machine (SVM) dan Chi Square Putu Agus Prawira Dharma Yuda; Cokorda Pramartha; I Komang Ari Mogi
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 3 (2024): JELIKU Volume 12 No 3, February 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2023.v12.i03.p21

Abstract

In an era marked by widespread computer usage, security emerges as a critical focal point demanding meticulous attention. The spectrum of potential threats encompasses various methods of attacking computer systems, with Denial of Service (DoS) attacks being a prominent concern. This study delves into the enhancement of cybersecurity by implementing a system capable of discerning between DoS attack data and normal data, employing the Support Vector Machine (SVM) algorithm. To optimize the efficacy of the classification system, a strategic feature selection process is imperative. This research advocates for the utilization of the Chi-square method for this purpose, aiming to eliminate irrelevant features and thereby enhance system performance. The Support Vector Machine algorithm, hinging on hyperplanes for classification, gains efficiency through judicious feature selection. The empirical findings of this research unveil that employing Chi-square feature selection significantly elevates the performance of the classification system when dealing with application layer attacks. Remarkably, this enhancement is achieved without compromising the accuracy of the system. Specifically, the classification of DoS application layer attacks using SVM in tandem with Chi-square yielded identical accuracy results compared to using SVM alone. The average accuracy reached an impressive 99.9995%, with a processing time of 6.08 minutes. In contrast, the classification system without feature selection consumed a comparatively longer processing time of 6.85 minutes. This underscores the efficacy of Chi-square feature selection in optimizing the performance of cybersecurity systems, demonstrating a streamlined approach to safeguarding computer networks from malicious threats.
Implementasi Metode Fuzzy Logic untuk Mendeteksi Asap Dupa di Pasar Tradisional Bali Berbasis IoT Ira Arituddiniyah; Cokorda Pramartha; I Dewa Made Bayu Atmaja Darmawan; I Gede Surya Rahayuda
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 13 No 1 (2024): JELIKU Volume 13 No 1, August 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

This research employs Fuzzy Logic for incense smoke detection in Bali's traditional markets, supported by IoT. It aims to develop a system for automatic identification, user reminders, and online monitoring. By using Fuzzy Logic, the system assesses smoke concentration, enabling appropriate responses. IoT integration facilitates object connectivity and communication. The outcome is a web platform for smoke monitoring, notifications, and remote device control. This study innovatively merges IoT with local wisdom, offering a valuable contribution.
Rancang Bangun Helpdesk System Berbasis Website dengan Codeigniter di PT Dimata Sora Jayate Artanta Wibawa, Putu Widyantara; Cokorda Pramartha; I Gusti Agung Gede Arya Kadyanan
Jurnal Pengabdian Informatika Vol. 3 No. 1 (2024): JUPITA Volume 3 Nomor 1, November 2024
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

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Abstract

Pesatnya perkembangan teknologi saat ini telah meningkatkan jumlah konsumen yang dimiliki perusahaan. Untuk dapat memberikan pelayanan yang terbaik bagi konsumen, perusahaan harus mampu membentuk hubungan baik dengan konsumen melalui pelayanan terhadap keluhan atau pertanyaan yang dimiliki oleh konsumen. Oleh karena itu, diperlukan sebuah sistem yang mampu untuk melakukan manajemen terhadap keluhan dan pertanyaan yang dimiliki oleh konsumen. Untuk mengatasi permasalahan tersebut, akan dikembangkan sebuah helpdesk system berbasis website di PT Dimata Sora Jayate yang mampu melakukan manajemen keluhan maupun pertanyaan menggunakan framework PHP yaitu CodeIgniter dengan database MySQL. Adapun metode yang digunakan dalam pengembangan helpdesk system adalah Design Science Research Methodology (DSRM) dengan empat tahapan utama, yaitu analisis penelitian, desain, implementasi, dan evaluasi. Helpdesk system yang telah berhasil dibangun kemudian diuji dengan Black-box dan memberikan hasil sesuai di seluruh aspek fitur yang diuji, sehingga sudah dapat digunakan untuk manajemen keluhan maupun pertanyaan dari pengguna secara efektif dan efisien.
Implementasi Model Poisson Laplace untuk IR pada E-Skripsi Universitas Udayana I Putu Andhika Ardianta Putra; Cokorda Pramartha
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 2 (2026): JNATIA Vol. 4, No. 2, Februari 2026
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2026.v04.i02.p13

Abstract

At Udayana University, a digital repository system is available to store books and student theses. However, the current repository system only categorizes documents based on the year of publication, without any grouping based on topics, fields of science, or other data. Although a search feature is available, the function has not been optimized. To overcome this problem, research was conducted on the Probabilistic Information Retrieval Model, namely the Poisson Model with Laplace Smoothing and Normalization 2. Research was conducted on student thesis title data in 2024 as many as 4,671 titles and evaluation using 10 queries. The research resulted in a Mean Average Precision value, normalized Discounted Cumulative Gain, and recall of 0.715 and a precision value of 0.1421. From this value, further experiments need to be held because the precision value is far different from the recall, indicating the number of False Positive values.
Analisis Sentimen pada Teks Berbahasa Bali Menggunakan Metode Multinomial Naive Bayes dengan TF-IDF dan BoW Putu Widyantara Artanta Wibawa; Cokorda Pramartha
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 1 (2023): JNATIA Vol. 2, No. 1, November 2023
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2023.v02.i01.p05

Abstract

Currently, digital technology is developing rapidly thus increasing the availability of textual data in the digital form. Many of the digital texts are available in Balinese. From the existing Balinese language texts, an analysis can be carried out to determine the emotional level or sentiment contained in them. Through this analysis, information will be obtained regarding sentiment towards a product or service so that it can be used as information for consideration in making decisions. To determine sentiment in textual data, more specifically unstructured data, several stages are required, one of which is feature extraction such as TF-IDF and BoW. This study will analyze the effect of TF-IDF and BoW feature extraction on the Multinomial Naive Bayes method. The test results show that the TF-IDF feature extraction provides precision, recall, accuracy, and F1-score values respectively 92.3%, 91.13%, 91.5%, and 91.38% higher than with BoW feature extraction. 
Optimasi Hyperparameter Algoritma Support Vector Machine dalam Klasifikasi Penyakit β-Thalassemia I Nyoman Adi Mahendra Putra; Cokorda Pramartha
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 2 (2025): JNATIA Vol. 3, No. 2, Februari 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i02.p07

Abstract

Beta-thalassemia, a type of thalassemia disease caused by genetic variations, forces sufferers to receive regular blood transfusions for survival. Therefore, classification of this disease is important to reduce the number of births of beta-thalassemia patients in the future. 5066 beta-thalassemia carrier patient data from the Punjab Thalassemia Prevention and Program (PTPP) case study was taken as the source in this study which was accessed through the github.com website. Preprocessing is done for class alignment to avoid data imbalance, feature selection to streamline model performance, and normalization of feature values to a certain scale. In this research, the main focus is on applying the performance of the Support Vector Machine (SVM) algorithm to obtain classification results. Before entering the final model, hyperparameter tuning is required to obtain suitable parameter values to be entered into the model. hyperparameter tuning that will be carried out include "C" (regularization parameter), "kernel" (kernel type), "gamma" (kernel parameter for non-linear kernels), and "degree" (polynomial degree for polynomial kernels), carried out before the model is evaluated. The accuracy results were evaluated using confusion matrix, resulting in precision of 99.81%, recall of 99.62%, f1-score of 99.71%, and accuracy of 99.71% after hyperparameter tuning where the best parameters are "'C': 1, 'gamma': 100, 'kernel': 'rbf'" with an average test score of 0.993494149 
Segmentasi Pengguna Spotify Berdasarkan Preferensi Musik dengan Algoritma K-Means Clustering Kadek Bisma Dharmasena; Cokorda Pramartha
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 1 (2024): JNATIA Vol. 3, No. 1, November 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v03.i01.p05

Abstract

Music streaming platforms like Spotify have become integral to the daily lives of millions globally, offering personalized listening experiences. However, managing a vast music catalog to present relevant content to each user remains a challenge. This study explores the application of the Kmeans clustering algorithm to segment Spotify users based on their music preferences. The goal is to group users into clusters with similar tastes to enhance targeted marketing and user engagement. We utilized a secondary dataset of trending Spotify songs and their attributes from 2023. Through data preprocessing, feature selection, and normalization, we prepared the data for clustering. The optimal number of clusters was determined using the Elbow Method, resulting in six distinct clusters. Each cluster represents unique music preferences, analyzed through metrics such as danceability, energy, and popularity. The findings demonstrate that K-means clustering effectively identifies user segments, providing insights for improving personalized recommendations and marketing strategies. This research underscores the potential of machine learning in optimizing user experiences on music streaming platforms. 
Co-Authors Agus Muliantara Anak Agung Istri Ngurah Eka Karyawati Andien Rachma Fadillah Artanta Wibawa, Putu Widyantara Gusti Agung Diah Sri Ari Ningsih I Dewa Made Bayu Atmaja Darmawan I Gede Surya Rahayuda I Gusti Agung Gede Arya Kadyanan I Gusti Bgs Darmika Putra I Komang Ari Mogi I Made Widhi Wirawan I Nyoman Adi Mahendra Putra I Nyoman Dheva Surya I Putu Agus Arya Wiguna I Putu Andhika Ardianta Putra Ira Arituddiniyah Kadek Bisma Dharmasena Komang Kartika Noviyanti Ni Luh Gede Ananda Puja Cahyani Ni Nyoman Sukma Prasetyadewi Gita Ni Putu Sri Agnita Samyami Wiraputri Ni Wayan Sani Utari Dewi Putu Agus Prawira Dharma Yuda Putu Widyantara Artanta Wibawa Raharja, Made Agung Ryan Hangralim Viencent